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test_depth_scaling.py
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test_depth_scaling.py
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"""
This file will train the depth part!
"""
import os
import cv2
import yaml
import json
import torch # Sorry to the google supervisors
import shutil
import numpy as np
from tqdm import tqdm
import torch.nn.functional as F
import matplotlib.pyplot as plt
from collections import OrderedDict
# from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
#from torchviz import make_dot, make_dot_from_trace
from torchvision.utils import save_image
# Imports from our files
from loss.losses import *
from utils.training_utils import *
from utils.arguments import arguments
from depth_estimation.networks import *
from utils.modify_images import corrupt_rgbd
from slam.custom_slam import image_recover_slam
from utils.yaml_configs import load_yaml, save_yaml
from utils.advanced_vis import plotly_map_update_visualization
from depth_estimation.view_synthesis import BackprojectDepth, Project3D
# GradSLAM Imports
import gradslam as gs
from gradslam.datasets import ICL, TUM
from gradslam.slam import ICPSLAM
from gradslam.slam import PointFusion
from chamferdist import ChamferDistance
from gradslam import Pointclouds, RGBDImages
class Depth_Estimation:
def __init__(self, arguments):
self.args = arguments
self.device = torch.device("cuda" if self.args.SETTINGS.device == "cuda" else "cpu")
self.sequence_length = len(self.args.DATA.frames)
self.color_map = plt.cm.get_cmap("magma").reversed()
#self.writer = SummaryWriter()
self.dataset_init()
self.model_init()
self.view_reconstruction_init()
self.losses_init()
if self.args.ABLATION.scale_intrinsics:
print("Scaling Intrinsics")
if self.args.ABLATION.scaled_depth:
print("Scaling Depth Maps")
def dataset_init(self):
"""
Initialize datasets in this function
Input:
None
Output:
None
"""
print("Loading Images of Size {} x {}".format(self.args.DATA.width, self.args.DATA.height))
self.data_path = os.path.join(self.args.DATA.data_path, self.args.DATA.name)
if self.args.DATA.name == "ICL":
traj = "living_room_traj0_frei_png"
mylist = []
mylist.append(traj)
traj_tuple = tuple(mylist)
self.dataset = ICL(basedir=self.data_path,
trajectories=traj_tuple,
seqlen=self.sequence_length,
height=self.args.DATA.height,
width=self.args.DATA.width,
dilation=self.args.DATA.dilation,
stride=self.args.DATA.stride,
start=self.args.DATA.start)
elif self.args.DATA.name == "TUM":
self.dataset = TUM(basedir=self.data_path,
seqlen=self.sequence_length,
height=self.args.DATA.height,
width=self.args.DATA.width,
dilation=self.args.DATA.dilation,
stride=self.args.DATA.stride,
start=self.args.DATA.start)
self.train_loader = DataLoader(dataset=self.dataset,
batch_size=self.args.OPTIMIZATION.batch_size,
shuffle=False,
num_workers=self.args.SETTINGS.num_workers,
pin_memory=True,
drop_last=True)
print("{} Dataset Loaded".format(self.args.DATA.name))
def model_init(self):
"""
Define all training models in here
Variables To Consider:
models: contains the list of all models
train_params: contains the trainable params
"""
self.models = {}
self.train_params = []
print("Initializing Models")
if self.args.MODEL.slam == "ICPSLAM":
self.models["SLAM"] = ICPSLAM(odom=self.args.MODEL.odom,
numiters=self.args.MODEL.numiters,
device=self.device)
self.models["GT_SLAM"] = ICPSLAM(odom="gt", device=self.device)
elif self.args.MODEL.slam == "PointFusion":
self.models["SLAM"] = PointFusion(odom=self.args.MODEL.odom,
dist_th=self.args.MODEL.dist_th,
angle_th= self.args.MODEL.angle_th,
sigma= self.args.MODEL.sigma,
numiters=self.args.MODEL.numiters,
device=self.device)
self.models["GT_SLAM"] = PointFusion(odom="gt", device=self.device)
print("Using the {} for SLAM".format(self.args.MODEL.slam))
"Resnet Encoder"
if self.args.MODEL.depth_network == "monodepth2":
self.models["depth_encoder"] = ResnetEncoder(self.args.MODEL.num_layers,
self.args.MODEL.weights_init_encoder == "imagenet")
self.models["depth_encoder"].to(self.device)
self.train_params += list(self.models["depth_encoder"].parameters())
"Depth Decoder"
self.models["depth_decoder"] = DepthDecoder(self.models["depth_encoder"].num_ch_enc,
self.args.DATA.scales)
self.models["depth_decoder"].to(self.device)
print("Loaded ResNet{} based depth network".format(self.args.MODEL.num_layers))
self.train_params += list(self.models["depth_decoder"].parameters())
elif self.args.MODEL.depth_network == "indoor":
self.models["depth"] = DispResNet_Indoor(num_layers=self.args.MODEL.num_layers,
pretrained=self.args.MODEL.weights_init_encoder == "imagenet")
self.train_params += list(self.models["depth"].parameters())
self.models["depth"].to(self.device)
else:
raise ValueError("Given {} is not a valid depth network option".format(self.args.MODEL.depth_network))
if self.args.MODEL.use_pretrained_models and self.args.MODEL.depth_network == "monodepth2":
self.load_model()
elif self.args.MODEL.use_pretrained_models and self.args.MODEL.depth_network == "indoor":
self.load_model_indoor()
self.optimizer = define_optim(self.args, self.train_params)
self.schedular = define_schedular(self.args, self.optimizer)
if self.args.OPTIMIZATION.load_optimizer and self.args.MODEL.load_depth_path:
self.load_optimizer()
elif self.args.OPTIMIZATION.load_optimizer and not self.args.MODEL.load_depth_path:
raise ValueError("Load optimizer only if pretrained depth is used !! Set Flag off!")
def view_reconstruction_init(self):
self.backproject_depth = BackprojectDepth(self.args.OPTIMIZATION.batch_size,
self.args.DATA.height,
self.args.DATA.width)
self.backproject_depth.to(self.device)
self.project_3d = Project3D(self.args.OPTIMIZATION.batch_size,
self.args.DATA.height,
self.args.DATA.width)
self.project_3d.to(self.device)
def losses_init(self):
self.ssim = SSIM()
self.ssim = self.ssim.to(self.device)
self.chamfer = ChamferDistance()
self.chamfer = self.chamfer.to(self.device)
def set_refinement_mode(self):
"""Convert depth model to refinement mode:
batch norm is in eval mode + frozen params
"""
# try using frozen batch norm?
for m in self.models.values():
m.eval()
for name, param in m.named_parameters():
if name.find("bn") != -1:
param.requires_grad = False
def train(self):
"""
Main Training Loop
"""
self.epoch = 0
self.step = 0
if self.args.MODEL.refinement_mode:
self.set_refinement_mode()
print("SLAM Reconstruction Started")
"Initialize a small sequence, "
for iter, batch in enumerate(self.train_loader):
colors, gt_depths, intrinsics, poses, transform = batch[0], batch[1], batch[2], batch[3], batch[4]
colors /= 255.0
colors, gt_depths, intrinsics, poses, transform = colors.to(self.device), \
gt_depths.to(self.device), \
intrinsics.to(self.device), \
poses.to(self.device), \
transform.to(self.device)
rgbd = RGBDImages(colors, gt_depths, intrinsics, poses)
self.gt_reconstruction, _ = self.models["GT_SLAM"](rgbd)
self.gt_reconstruction = self.gt_reconstruction.detach()
"""
Depth Estimation
Akbar TODO: GradSLAM has channel last implementation while conv2d in pytorch expects channel first.
Gradslam support channel first but I am not sure if we need to reconvert to channel last while we do the slam part.
Akbar Answer: yes we do actually. The SLAM part expects channel last representation ,
maybe ask krishna to add support for channel first
"""
scale = 0 # Only using 1 scale for now. kinda redundent for now (remove usage if not needed)
self.initial_depths = {}
for refine_step in range(self.args.OPTIMIZATION.refinement_steps):
"""
Notes: Outputs is a dict that contains the depth prediction for an entire sequence.
Convert Depth Sequence stored in dictonary to depth sequence stored as a Tensor (to make it similar to gradslam)
"""
inputs = OrderedDict() # Used for the depth part
encoder_features = []
depth_tensor = [] # Used for the SLAM part
for index in range(self.sequence_length):
if self.args.MODEL.depth_network == "monodepth2":
encoder_features.append(self.models["depth_encoder"](colors[:, index, ...]))
inputs.update(self.models["depth_decoder"](encoder_features[index], index))
# Convert Disparity into Depth
inputs[("depth", index, scale)] = convert_disp_to_depth(inputs[("disp", index, scale)],
self.args.DATA.min_depth,
self.args.DATA.max_depth)
if self.args.ABLATION.scale_intrinsics:
focal_data = intrinsics[0, 0, 0, 0]
focal_pretrain = self.args.ABLATION.focal_pretrain
inputs[("depth", index, scale)] = scale_by_f(focal_data=focal_data,
focal_pretrain=focal_pretrain,
depth= inputs[("depth", index, scale)])
if self.args.ABLATION.scaled_depth:
if self.args.ABLATION.with_bias:
inputs[("depth", index, scale)] = inputs[("depth", index, scale)] * self.args.ABLATION.scaling_depth + self.args.ABLATION.bias
else:
inputs[("depth", index, scale)] *= self.args.ABLATION.scaling_depth
if self.args.OPTIMIZATION.refinement == "PFT" and self.args.LOSS.depth_regularizer and refine_step == 0:
self.initial_depths[("initial_depth", index, scale)] = inputs[("depth", index, scale)].copy().detach()
inputs[("gt_depth", index, scale)] = gt_depths[:, index, ...]
depth_tensor.append(inputs[("depth", index, scale)].unsqueeze(1)) # Unsqueeze to create Sequence Dimension
elif self.args.MODEL.depth_network == "indoor":
inputs.update(self.models["depth"](colors[:, index, ...], index))
# Indoor model does not do this scaling... this creates a noticible difference since the error between
# 1/disp and 1/scaled disp is 0.3 units.
# outputs[("depth", index, scale)] = convert_disp_to_depth(outputs[("disp", index, scale)],
# self.args.DATA.min_depth,
# self.args.DATA.max_depth)
inputs[("depth", index, scale)] = 1 / inputs[("disp", index, scale)]
if self.args.ABLATION.scale_intrinsics:
focal_data = intrinsics[0, 0, 0, 0]
focal_pretrain = self.args.ABLATION.focal_pretrain
inputs[("depth", index, scale)] = scale_by_f(focal_data=focal_data,
focal_pretrain=focal_pretrain,
depth= inputs[("depth", index, scale)])
if self.args.ABLATION.scaled_depth:
if self.args.ABLATION.with_bias:
inputs[("depth", index, scale)] = inputs[("depth", index, scale)] * self.args.ABLATION.scaling_depth + self.args.ABLATION.bias
else:
inputs[("depth", index, scale)] *= self.args.ABLATION.scaling_depth
if self.args.OPTIMIZATION.refinement == "PFT" and self.args.LOSS.depth_regularizer and refine_step == 0:
self.initial_depths[("initial_depth", index, scale)] = inputs[("depth", index, scale)].clone().detach()
depth_tensor.append(inputs[("depth", index, scale)].unsqueeze(1)) # Unsqueeze to create Sequence Dimension
inputs[("gt_depth", index, scale)] = gt_depths[:, index, ...]
del encoder_features # Free Space
depth_tensor = torch.cat(depth_tensor, dim=1) # use this for SLAM!
depth_tensor = depth_tensor.permute(0, 1, 3, 4, 2) # Change to channel last representation
if self.args.DATA.use_gt_pose:
new_poses = poses
else:
if self.step == 0: # Initially the pose is taken as identity
new_poses = torch.eye(4, device=self.device).view(1, 1, 4, 4).repeat(self.args.OPTIMIZATION.batch_size,
self.sequence_length, 1, 1)
noisy_rgbd = RGBDImages(rgb_image=colors,
depth_image=depth_tensor,
intrinsics=intrinsics,
poses=new_poses)
if self.args.DATA.use_gt_pose:
"""
We get rid of poses from SLAM if we using GT Pose, otherwise we will take the new pose from the SLAM
and feed it back into the network.
"""
noisy_reconstruction, _ = self.models["SLAM"](noisy_rgbd)
new_transform = transform
else:
noisy_reconstruction, new_poses = self.models["SLAM"](noisy_rgbd)
new_transform = torch_poses_to_transforms(new_poses)
noisy_pointcloud = noisy_reconstruction.points_list[0].unsqueeze(0).contiguous() # Only optimizing points not color!
inputs[("noisy_pointcloud")] = noisy_pointcloud
if self.step == 0 and self.args.VIZ.plot_first_step or self.step % 10 == 0: # TODO : Plotting here
plt.imshow(inputs[("depth", 1, 0)][0].detach().cpu().squeeze(), cmap=self.color_map)
plt.axis('off')
plt.show()
#noisy_reconstruction.plotly(0).show()
"--- Refinement of Depth Maps ---"
total_loss = self.depth_refinement(colors, inputs, intrinsics, new_transform)
new_poses = new_poses.detach() # TODO: Careful :: But it worked in GradSLAM??
self.step += 1
if self.args.DEBUG.print_metrics:
abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3 = depth_metrics(dataset=self.args.DATA.name,
gt=gt_depths[0][1],
pred=inputs[("depth", 1, 0)][0])
print("Iter:", iter,
"Refine_Step:", refine_step,
"Total_Loss:", round(total_loss, 5),
"abs_rel: ", round(abs_rel.item(), 5),
"rmse: ", round(rmse.item(), 5),
"a1: ", round(a1.item(), 5))
else:
print("Iter:", iter,
"Refine_Step:", refine_step,
"Total_Loss:", round(total_loss, 5))
if (refine_step + 1) % 6 == 0:
#save_image(inputs[("depth", 1, 0)][0].detach().cpu().squeeze(), '/cluster/scratch/semilk/ICL/results/depth_map' + str(int((refine_step+1)/6)) + '.png', scale_each=True)
#save_image(inputs[("depth", 1, 0)][0].detach().cpu().squeeze(), '/cluster/scratch/semilk/ICL/results/depth_map.png')
plt.imshow(inputs[("depth", 1, 0)][0].detach().cpu().squeeze(), cmap=self.color_map)
plot_path = '/cluster/scratch/semilk/ICL/results/depth_map' + str(int((refine_step+1)/6)) + '.png'
plt.axis('off')
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0, dpi=1200)
self.schedular.step()
if self.args.DEBUG.early_stop and iter == self.args.DEBUG.iter_stop:
break
# Final plot after training. [Maybe we should perform validation step on other scenes]
if self.args.VIZ.plot_final_step:
plt.imshow(inputs[("depth", 1, 0)][0].detach().cpu().squeeze(), cmap=self.color_map)
plt.axis('off')
plt.show()
noisy_reconstruction.plotly(0).show()
if self.args.VIZ.plot_gt:
self.gt_reconstruction.plotly(0).show()
# Save depth map for test
#save_image(inputs[("depth", 0, 0)][0].detach().cpu().squeeze(), '/cluster/scratch/semilk/depth_map_test_img.png')
def depth_refinement(self, colors, inputs, intrinsics, poses):
"""
Essentially Unsupervised Depth Estimation in refinement steps.
outputs only contain the outputs from the view synthesis module, NOT initial data that is available from other sources
"""
outputs = {}
inputs.update(self.process_inputs(colors, inputs, intrinsics, poses))
outputs.update(self.novel_view_synthesis(inputs))
loss = self.compute_losses(inputs, outputs)
return loss
def process_inputs(self, colors, inputs, intrinsics, poses):
"""
Process the inputs suitable for view sythesis. Easier to understand for others then.
NOTE: This function assumes sequence length of 2 or 3. For larger sequence lengths change appropriately.
Making everything channel first representation too for PyTorch.
"""
if self.sequence_length == 3:
inputs["source_frame", -1] = colors[:, 0, ...].permute(0, 3, 1, 2)
inputs["target_frame"] = colors[:, 1, ...].permute(0, 3, 1, 2)
inputs["source_frame", 1] = colors[:, 2, ...].permute(0, 3, 1, 2)
if self.args.MODEL.depth_network == "monodepth2" and self.args.DATA.normalize_intrinsics:
# TODO: SLAM is giving errors if i do this normalization.
inputs["K"] = normalize_intrinsics(self.args, intrinsics[:, 0,...]) # In a particular sequence, the intrinsic should be constant.
inputs["Inverse_K"] = torch.pinverse(inputs["K"])
else:
inputs["K"] = intrinsics[:, 0, ...] # In a particular sequence, the intrinsic should be constant.
inputs["Inverse_K"] = torch.pinverse(inputs["K"])
inputs["source_depth", -1] = inputs["depth", 0, 0]
inputs["target_depth"] = inputs["depth", 1, 0]
inputs["source_depth", 1] = inputs["depth", 2, 0]
inputs["source_disp", -1] = inputs["disp", 0, 0]
inputs["target_disp"] = inputs["disp", 1, 0]
inputs["source_disp", 1] = inputs["disp", 2, 0]
# IF SEQUENCE LENGTH IS GREATER THAN 3, handle pose appropriately.
inputs["T", -1] = poses[:, 1, ...] # This represents the pose 0 -> -1
inputs["T", 1] = inverse_T_matrix(poses[:, 2, ...]) # This represents the pose 0 -> 1
# GT Depths
scale = 0
for index in range(len(self.args.DATA.frames)):
inputs[("sparse_gt_depth", index, scale)], inputs[("sparse_mask", index, scale)] = sparse_sampling(self.args.LOSS.sampling_type,
self.args.LOSS.sampling_prob,
inputs[("gt_depth", index, scale)])
elif self.sequence_length == 2:
if self.args.DATA.frames[1] < 0:
inputs["source_frame", -1] = colors[:, 0, ...].permute(0, 3, 1, 2)
inputs["target_frame"] = colors[:, 1, ...].permute(0, 3, 1, 2)
if self.args.MODEL.depth_network == "monodepth2" and self.args.DATA.normalize_intrinsics:
# TODO: SLAM is giving errors if i do this normalization.
inputs["K"] = normalize_intrinsics(self.args, intrinsics[:, 0,
...]) # In a particular sequence, the intrinsic should be constant.
inputs["Inverse_K"] = torch.pinverse(inputs["K"])
else:
inputs["K"] = intrinsics[:, 0, ...] # In a particular sequence, the intrinsic should be constant.
inputs["Inverse_K"] = torch.pinverse(inputs["K"])
inputs["source_depth", -1] = inputs["depth", 0, 0]
inputs["target_depth"] = inputs["depth", 1, 0]
inputs["source_disp", -1] = inputs["disp", 0, 0]
inputs["target_disp"] = inputs["disp", 1, 0]
inputs["T", -1] = poses[:, 1, ...] # This represents the pose 0 -> -1
elif self.args.DATA.frames[1] > 0:
inputs["target_frame"] = colors[:, 0, ...].permute(0, 3, 1, 2)
inputs["source_frame", 1] = colors[:, 1, ...].permute(0, 3, 1, 2)
if self.args.MODEL.depth_network == "monodepth2" and self.args.DATA.normalize_intrinsics:
# TODO: SLAM is giving errors if i do this normalization.
inputs["K"] = normalize_intrinsics(self.args, intrinsics[:, 0,
...]) # In a particular sequence, the intrinsic should be constant.
inputs["Inverse_K"] = torch.pinverse(inputs["K"])
else:
inputs["K"] = intrinsics[:, 0, ...] # In a particular sequence, the intrinsic should be constant.
inputs["Inverse_K"] = torch.pinverse(inputs["K"])
inputs["target_depth"] = inputs["depth", 0, 0]
inputs["source_depth", 1] = inputs["depth", 1, 0]
inputs["target_disp"] = inputs["disp", 0, 0]
inputs["source_disp", 1] = inputs["disp", 1, 0]
inputs["T", 1] = inverse_T_matrix(poses[:, 1, ...])
else:
raise ValueError("Sequence Length of 2 and 3 is only supported")
return inputs
def novel_view_synthesis(self, inputs):
outputs = {}
for frame in self.args.DATA.frames[1:]:
camera_points = self.backproject_depth(inputs["target_depth"], inputs["Inverse_K"])
if self.args.DEBUG.plot and self.step % 10 == 0:
"Plots photometric error maps and saves them in the given location"
plt.imshow(inputs["target_depth"].detach().cpu().squeeze(), cmap=self.color_map)
plot_path = os.path.join(self.args.DEBUG.plot_path + "/depth_{}".format(self.step))
plt.axis('off')
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0, dpi=1200)
if self.args.LOSS.geometric:
pixel_coordinates, warped_depth, valid_mask = self.project_3d(points=camera_points,
K=inputs["K"],
T=inputs["T", frame],
geometric=True)
outputs[("warped_depth", frame)] = warped_depth
outputs[("valid_mask", frame)] = valid_mask
outputs[("synthesized_frame", frame)] = F.grid_sample(inputs["source_frame", frame],
pixel_coordinates,
padding_mode=self.args.MODEL.padding_mode,
align_corners=True)
outputs[("interpolated_depth", frame)] = F.grid_sample(inputs["source_depth", frame],
pixel_coordinates,
padding_mode=self.args.MODEL.padding_mode,
align_corners=False)
else:
pixel_coordinates, valid_mask = self.project_3d(points=camera_points,
K=inputs["K"],
T=inputs["T", frame],
geometric=False)
outputs[("valid_mask", frame)] = valid_mask
outputs[("synthesized_frame", frame)] = F.grid_sample(inputs["source_frame", frame],
pixel_coordinates,
padding_mode=self.args.MODEL.padding_mode,
align_corners=False)
if self.args.DEBUG.plot and self.step == 0:
"Plots photometric error maps and saves them in the given location"
plt.imshow(inputs["target_frame"].detach().cpu().squeeze().permute(1,2,0))
plot_path = os.path.join(self.args.DEBUG.plot_path + "/tF_{}".format(self.step))
plt.axis('off')
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0, dpi=1200)
if self.args.DEBUG.plot and self.step == 0:
"Plots photometric error maps and saves them in the given location"
plt.imshow(inputs["source_frame", frame].detach().cpu().squeeze().permute(1,2,0))
plot_path = os.path.join(self.args.DEBUG.plot_path + "/sF_{}".format(self.step))
plt.axis('off')
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0, dpi=1200)
if self.args.DEBUG.plot and self.step % 10 == 0:
"Plots photometric error maps and saves them in the given location"
plt.imshow(outputs["synthesized_frame", frame].detach().cpu().squeeze().permute(1,2,0))
plot_path = os.path.join(self.args.DEBUG.plot_path + "/synthF_{}".format(self.step))
plt.axis('off')
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0, dpi=1200)
plt.close("all")
return outputs
def compute_losses(self, inputs, outputs):
losses = {}
loss = 0
self.optimizer.zero_grad()
photmetric = self.compute_photometric_loss(inputs=inputs,
outputs=outputs)
if self.args.LOSS.min_reprojection:
photmetric = photmetric
else:
photmetric = photmetric.mean(1, keepdim=True)
if self.args.DEBUG.plot and self.args.LOSS.min_reprojection and self.step % 10 == 0:
"Plots photometric error maps and saves them in the given location"
plt.imshow(torch.min(photmetric, dim=1)[0].detach().cpu().squeeze(), cmap="binary")
plot_path = os.path.join(self.args.DEBUG.plot_path + "/pE_{}".format(self.step))
plt.axis('off')
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0, dpi=1200)
elif self.args.DEBUG.plot and self.step % 10 == 0:
plt.imshow(photmetric[0].detach().cpu().squeeze(), cmap="binary")
plot_path = os.path.join(self.args.DEBUG.plot_path + "/pE_{}".format(self.step))
plt.axis('off')
plt.savefig(plot_path, bbox_inches="tight", pad_inches=0, dpi=1200)
if self.args.LOSS.auto_masking:
auto_masking = self.compute_automasking_loss(inputs=inputs,
outputs=outputs)
if self.args.LOSS.min_reprojection:
auto_masking += torch.randn(auto_masking.shape).cuda() * 0.00001 # Break tie's
else:
auto_masking = auto_masking.mean(1, keepdim=True)
photmetric = torch.cat((auto_masking, photmetric), dim=1)
if photmetric.shape[1] == 1:
optimize = photmetric
optimize = optimize.mean()
else:
optimize, indexs = torch.min(photmetric, dim=1)
optimize = optimize.mean()
loss += optimize
losses["photometric_loss"] = optimize.item()
if self.args.LOSS.geometric:
geometric = self.compute_geometric_loss(outputs=outputs)
geometric = geometric.mean()
loss += geometric * self.args.LOSS.geometric_weight
losses["geometric_loss"] = geometric.item()
if self.args.LOSS.smoothness:
smooth_loss = self.compute_smoothness_loss(inputs=inputs)
loss += smooth_loss * self.args.LOSS.smoothness_weight
losses["smoothn_loss"] = smooth_loss.item()
if self.args.LOSS.depth_regularizer:
depth_reg = self.compute_depth_regularizer(inputs=inputs)
loss += depth_reg * self.args.LOSS.depth_regularizer_weight
losses["depth regularizer"] = depth_reg.item()
if self.args.LOSS.knn_points:
knn_loss, indexs = knn_points_loss(gt_pointcloud=self.gt_reconstruction.points_list[0].unsqueeze(0).contiguous(),
noisy_pointcloud=inputs["noisy_pointcloud"])
loss += knn_loss * self.args.LOSS.knn_points_weight
print("knn_loss", knn_loss.item()) # Turn off unless debug
if self.args.LOSS.chamfer_distance:
chamfer_dist = 0.5 * self.chamfer(inputs["noisy_pointcloud"],
self.gt_reconstruction.points_list[0].unsqueeze(0).contiguous(),
bidirectional=True)
loss += chamfer_dist * self.args.LOSS.chamfer_weight
print("chamfer_loss", chamfer_dist.item()) # Turn off unless debug
if self.args.LOSS.supervise_depth:
gt_loss = self.compute_gt_depth_loss(inputs=inputs)
loss += gt_loss * self.args.LOSS.gt_depth_weight
losses["gt_depth_loss"] = gt_loss.item()
loss.backward()
self.optimizer.step()
return loss.item()
def compute_photometric_loss(self, inputs, outputs):
"""
Compute the photometric loss here
"""
photometric_losses = []
for frame in self.args.DATA.frames[1:]:
if self.args.LOSS.photometric_mask:
masked_prediction = outputs[("synthesized_frame", frame)] * outputs["valid_mask", frame]
masked_target = inputs[("target_frame")] * outputs["valid_mask", frame]
photometric_losses.append(photometric_loss(ssim=self.ssim,
prediction=masked_prediction,
target=masked_target))
else:
prediction = outputs[("synthesized_frame", frame)]
target = inputs[("target_frame")]
photometric_losses.append(photometric_loss(ssim=self.ssim,
prediction=prediction,
target=target))
photometric_losses = torch.cat(photometric_losses, 1)
return photometric_losses
def compute_automasking_loss(self, inputs, outputs):
"""
Compute identity losses for auto-masking technique by monodepth2
"""
auto_masking_losses = []
for frame in self.args.DATA.frames[1:]:
if self.args.LOSS.photometric_mask:
masked_prediction = inputs[("source_frame", frame)] * outputs["valid_mask", frame]
masked_target = inputs[("target_frame")] * outputs["valid_mask", frame]
auto_masking_losses.append(photometric_loss(ssim=self.ssim,
prediction=masked_prediction,
target=masked_target))
else:
prediction = inputs[("source_frame", frame)]
target = inputs[("target_frame")]
auto_masking_losses.append(photometric_loss(ssim=self.ssim,
prediction=prediction,
target=target))
auto_masking_losses = torch.cat(auto_masking_losses, 1)
return auto_masking_losses
def compute_geometric_loss(self, outputs):
"""
Compute the geometric loss here
"""
geometric_losses = []
for frame in self.args.DATA.frames[1:]:
geometric_losses.append(geometric_consistency_loss(outputs, frame, self.device))
geometric_losses = torch.stack(geometric_losses, dim=0)
return geometric_losses
def compute_smoothness_loss(self, inputs):
"""
compute the smoothness loss here on the disparities! Not depths.
"""
disparity = inputs[("disp", 0, 0)]
mean_disparity = disparity.mean(2, True).mean(3, True)
norm_disparity = disparity / (mean_disparity + 1e-7)
smooth_loss = disparity_smoothness_loss(disp=norm_disparity,
img=inputs[("target_frame")])
return smooth_loss
def compute_depth_regularizer(self, inputs):
"""
This function computes a regularizer such that the refined depth does not sway too far from the initial estimate of depth.
"""
scale = 0
reg = 0
for frame in range(len(self.args.DATA.frames)): # TODO: Improve convention of how initial_depth and predicted depth are stored maybe.
reg += depth_reguralizer(initial_depth=self.initial_depths[("initial_depth", frame, scale)],
refined_depth=inputs[("depth", frame, scale)],
loss_func=self.args.LOSS.depth_regularizer_type)
return reg
def compute_gt_depth_loss(self, inputs):
gt_loss = 0
scale = 0
for frame in range(len(self.args.DATA.frames)):
gt_loss += depth_gt_loss(prediction=inputs[("depth", frame, scale)],
sparse_groundtruth=inputs[("sparse_gt_depth", frame, scale)],
sparse_mask=inputs[("sparse_mask", frame, scale)])
return gt_loss
def load_model(self):
"""
Load pretrained models from disk.
Variables To Consider:
self.models: Contains List of All Trainable Models.
Flags To Consider:
MODEL.load_depth_path: Path to pretrained models and their optimizers for resume training
MODEL.pretrained_models_list: List of models to load
"""
self.args.MODEL.load_depth_path = os.path.expanduser(self.args.MODEL.load_depth_path)
assert os.path.isdir(self.args.MODEL.load_depth_path), "Cannot find folder {}".format(self.args.MODEL.load_depth_path)
print("loading model from folder {}".format(self.args.MODEL.load_depth_path))
for n in self.args.MODEL.pretrained_models_list:
print("Loading {} weights...".format(n))
path = os.path.join(self.args.MODEL.load_depth_path, "{}.pth".format(n))
model_dict = self.models[n].state_dict()
pretrained_dict = torch.load(path)
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
self.models[n].load_state_dict(model_dict)
def load_model_indoor(self):
"""
Load pretrained models from disk.
Variables To Consider:
self.models: Contains List of All Trainable Models.
Flags To Consider:
MODEL.load_depth_path: Path to pretrained models and their optimizers for resume training
MODEL.pretrained_models_list: List of models to load
"""
self.args.MODEL.load_depth_path = os.path.expanduser(self.args.MODEL.load_depth_path)
assert os.path.isdir(self.args.MODEL.load_depth_path), "Cannot find folder {}".format(self.args.MODEL.load_depth_path)
print("loading model from folder {}".format(self.args.MODEL.load_depth_path))
n = "depth"
print("Loading {} weights...".format(n))
path = os.path.join(self.args.MODEL.load_depth_path, "{}.pth.tar".format(n))
pretrained_dict = torch.load(path)
self.models[n].load_state_dict(pretrained_dict["state_dict"])
print("Loaded Indoor Depth Model")
#TODO: Add Save_Model
def load_optimizer(self):
"""
Load Optimizer state dict if resuming training.
Flags To Consider:
MODEL.load_depth_path: Path to pretrained models and their optimizers for resume training
MODEL.pretrained_models_list: List of models to load
"""
optimizer_load_path = os.path.join(self.args.MODEL.load_depth_path, "{}.pth".format(self.args.OPTIMIZATION.optimizer))
if os.path.isfile(optimizer_load_path):
print("Loading Optimizer Weights")
optimizer_dict = torch.load(optimizer_load_path)
self.optimizer.load_state_dict(optimizer_dict)
else:
print("Optimizer Not Found. Randomly Initialized")
if __name__ == "__main__":
args = arguments()
config_path = args['config_path']
config_dict = load_yaml(config_path)
config_dict.SETTINGS.name = args['name']
SLAM = Depth_Estimation(config_dict)
SLAM.train()